Travis SmithHazardous Weather Forecasts & Warnings

Nowcasting Applications

2NSSL Laboratory Review February 17-19, 2009

“Nowcasting” Applications

Remotely sensed detection

0-2 hour warning / forecast

High temporal and spatial resolution

Assistance for warning decision-making.

Limitations of early algorithms

Entire feature reduced to a point

Need to quantify uncertainty, rather than a deterministic answer

Need better quality control and higher resolution data

Need all meteorological information that went into algorithm decision

Not easy to integrate data from multiple sensors

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Multiple sensors

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Blended 3D multi-radar data

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Radars in network supplement each other:

Overlapping coverageFills in gaps from terrain blockage Increased sampling frequency

Example: Blending data from multiple

sources

SatelliteRadarLightning

Height of -20C Isotherm

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Examples: Multi-sensor data

fieldsShow physical relationships between data fields from multiple sensors

Storm tracks and trends can be generated at any spatial scale, for any data fields

Near-surface reflectivity

Reflectivity @ -20 C

(~6.5 km AGL)

Total Lightning Density

Max Expected Size of Hail

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Users

We produce about 100 different data fields in real-time

Storm Prediction Center operations (& other NCEP)5 NWS Forecast Offices – direct feedsGoogle Earth layer: 12,000 unique users, 3.5M to 12M hits per month (including additional NWS users)Licensed to private industry: 46% of US TV stations

Transition to NWS operations once AWIPS2 deployed

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Hazardous Weather Testbed / Experimental

Warning ProgramContinued collaboration

with forecasters is vital!

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10NSSL Laboratory Review February 17-19, 2009

Ongoing / Future research

Integrating new data sources

Polarimetric radar dataPhased Array RadarTotal Lightning (GOES-R & ground-based sensors)

CONUS-scale 3D radar re-analysis at 1km / 5 min –

data mining1995 – present

0° C

0° C

Zdr “column”

Reflectivity

Cross-section

Summary

Science and applications to support nowcasting:

Multi-sensor applicationsForecaster-drivenProducts show physical process relationshipsMoving towards probabilistic hazard informationWide use across NWS and private sector

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